Image texture synthesis method based on the trend function constraint

Author(s):  
Yuanying Gan ◽  
Chuntong Liu ◽  
Zhenxin He ◽  
Hongcai Li ◽  
Zhongye Liu ◽  
...  
2014 ◽  
Vol 687-691 ◽  
pp. 4140-4147
Author(s):  
Hong Tao Peng ◽  
De Gan Zhang ◽  
Xiao Dong Song ◽  
Xiang Wang

Most patch-based texture synthesis algorithms using Markov Random Field for composite materials only considers color similarity between the corresponding pixels. The traditional algorithms are lack of adaptability, so the size of patches needs to be defined artificially in advance as the result of blurring of image texture features for composite materials. In order to improve above problems, a new patch-based sampling algorithm for synthesizing textures from an input sample image texture of composite materials is presented in this paper. By using patches of the sample texture as building blocks for image texture synthesis of composite materials, this algorithm makes high-quality texture synthesis for a wide variety of textures ranging regular to stochastic. The method is effective by our experimental results.


2014 ◽  
Vol 571-572 ◽  
pp. 825-828
Author(s):  
Xiang Zhang ◽  
Jun Hua Wang ◽  
Xiao Ling Xiao

The image inpainting method based on CriminiciA’s algorithm is slowly complete the image for large blank area. An improved algorithm based on the classic texture synthesis algorithm for image inpainting is proposed for imaging logging inpainting, which is used to generate the fullbore image. Two schemes, the local search method and priority calculation with TV model, are employed in the improved texture synthesis method. Some examples were given to demonstrate the effectiveness of the proposed algorithm on dealing with fullbore image construction with large blank area and raising efficiency obviously.


Author(s):  
Andrew Babichev ◽  
Vladimir Alexandrovich Frolov

In this paper we propose exemplar-based 3D texture synthesis method which unlike existing neural network approaches preserve structural elements in texture. The proposed approach does this by accounting additional image properties which stand for the preservation of the structure with the help of a specially constructed error function used for training neural networks. Thanks to the proposed solution we can apply 2D texture to any 3D model (even without texture coordinates) by synthesizing high quality 3D texture and using local or world space position of surface instead 2D texture coordinates (fig. 1). Our solution is based on introducing 3 different error components in to the process of neural network fitting which helps to preserve desired properties of generated texture. The first component is for structuredness of the generated texture and the sample, the second component increases the diversity of the generated textures and the third one prevents abrupt transitions between individual pixels.


1993 ◽  
Vol 29 (4) ◽  
pp. 1110-1122 ◽  
Author(s):  
J.A. Cadzow ◽  
D.M. Wilkes ◽  
R.A. Peters ◽  
X. Li

2015 ◽  
Vol 738-739 ◽  
pp. 573-577
Author(s):  
Xuan Zhu ◽  
Xu Feng Zhang ◽  
Qiu Ju Li ◽  
Ji Yao Tao ◽  
Ben Yuan Li

Redundant Discrete Wavelet Transform (RDWT) and Wavelet Atomic Transform (WAT) are proposed in this paper as a couple of dictionaries to get the structure and texture basing on morphological component decomposition. Then, basing on the fact that the structure and texture have different characteristics, in this paper we use curvature driven diffusion model and Criminisi texture synthesis method to inpaint the structure and texture respectively. At last, compound the inpainted structure and texture and get the inpainting result. The experiment results show the new method can not only decompose the image very well, but also inpaint the image with strong and fairing edge, complete and clear texture .This method shows better results in image inpainting compared to the classical ones.


Author(s):  
Adam W. Bargteil ◽  
Funshing Sin ◽  
Jonathan E. Michaels ◽  
Tolga G. Goktekin ◽  
James F. O'Brien

Sign in / Sign up

Export Citation Format

Share Document